Harness Engineering
Harness engineering is a proposed methodology for managing AI-assisted code generation by implementing deterministic tooling and periodic checks. It aims to prevent the 'code drift' that occurs when AI assistants ignore architectural conventions or internal consistency over time.
Why it matters
As AI coding assistants become standard, ensuring long-term codebase integrity is critical for preventing technical debt and security vulnerabilities.
Harness Engineering Harness engineering is the practice of surrounding AI-assisted code generation with deterministic tooling, agent-based review, and periodic entropy checks so that AI-generated code stays correct and coherent over time. This document explains where the idea came from, what it consists of, and how this plugin implements it.
The term comes from Birgitta Boeckeler's article on martinfowler.com, written in the ThoughtWorks context of teams shipping real software with AI coding assistants. Boeckeler observed something that many teams had noticed independently: AI assistants produce plausible-looking code, but left unconstrained they drift. They forget conventions, repeat mistakes, and slowly erode the internal consistency of a codebase. The code continues to compile and pass tests. The degradation is quiet.
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